TY - GEN A1 - Weber, Marcus A1 - Rungsarityotin, Wasinee A1 - Schliep, Alexander T1 - Perron Cluster Analysis and Its Connection to Graph Partitioning for Noisy Data N2 - The problem of clustering data can be formulated as a graph partitioning problem. Spectral methods for obtaining optimal solutions have reveceived a lot of attention recently. We describe Perron Cluster Cluster Analysis (PCCA) and, for the first time, establish a connection to spectral graph partitioning. We show that in our approach a clustering can be efficiently computed using a simple linear map of the eigenvector data. To deal with the prevalent problem of noisy and possibly overlapping data we introduce the min Chi indicator which helps in selecting the number of clusters and confirming the existence of a partition of the data. This gives a non-probabilistic alternative to statistical mixture-models. We close with showing favorable results on the analysis of gene expressi on data for two different cancer types. T3 - ZIB-Report - 04-39 KW - Perron cluster analysis KW - spectral graph theory KW - clustering KW - gene expression Y1 - 2004 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-8140 ER - TY - CHAP A1 - Weber, Marcus A1 - Rungsarityotin, Wasinee A1 - Schliep, Alexander ED - Spiliopoulou, Myra ED - Kruse, Rudolf ED - Borgelt, Christian ED - Nürnberger, Andreas ED - Gaul, Wolfgang T1 - An Indicator for the Number of Clusters T2 - From Data and Information Analysis to Knowledge Engineering Y1 - 2006 UR - http://dx.doi.org/10.1007/3-540-31314-1_11 SP - 103 EP - 110 PB - Springer Berlin Heidelberg ER -